Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/132879
Title: Transforming the induction programme at IPS : the journey towards a personalised adaptive learning system
Authors: Abdilla, Keona (2025)
Keywords: Student-centered learning -- Malta
Individualized education programs -- Malta
Issue Date: 2025
Citation: Abdilla, K. (2025). Transforming the induction programme at IPS: the journey towards a personalised adaptive learning system (Master's dissertation).
Abstract: Technological advancements have reshaped learning styles and behaviours, prompting organisations to prioritise personalised employee training. This study proposes the development of a Personalised Adaptive Learning System (PALS) as part of the Core Induction Programme for External Recruits provided by the Institute for the Public Services (IPS). Designed as a decision-support tool for training managers, PALS aims to bridge the gap between available courses and recruits’ needs. By identifying key elements crucial for its successful implementation, the system seeks to address medium- to long-term training requirements beyond the current standardised induction programme, offering personalised course recommendations. The study employed a concurrent transformative mixed-methods methodology using a questionnaire with both Likert-scale and open-ended questions to capture subjective and practical insights. The collected data were analysed using statistical and thematic analysis. The statistical analysis indicated that new recruits generally had positive perceptions on the current Core Induction Programme, though there were minor concerns regarding its informativeness and sufficiency, as well as greater concerns about the applicability of courses. Furthermore, recruits expressed strong support for integrating PALS into the induction process. The thematic analysis further identified key factors essential for the effective implementation of PALS, which guided its proposed architecture and addressed the second research question. In conclusion, based on initial findings, the proposed PALS architecture addressed the statistical concerns by ensuring personalised, relevant, and engaging course recommendations through adaptive learning technologies and the integration of feedback mechanisms. This ensures that the system remains dynamic and continuously refines its recommendations to effectively meet medium- to long-term training requirements.
Description: M.A.(Melit.)
URI: https://www.um.edu.mt/library/oar/handle/123456789/132879
Appears in Collections:Dissertations - FacEma - 2025
Dissertations - FacEMAMAn - 2025

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